Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,938 Bytes
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import os
import torch
import sys
import spaces
import gradio as gr
import random
from configs.infer_config import get_parser
from huggingface_hub import hf_hub_download
i2v_examples = [
['test/images/boy.png', 0, 1.0, '0 40', '0 0', '0 0', 50, 123],
['test/images/car.jpeg', 0, 1.0, '0 -35', '0 0', '0 -0.1', 50, 123],
['test/images/fruit.jpg', 0, 1.0, '0 -3 -15 -20 -17 -5 0', '0 -2 -5 -10 -8 -5 0 2 5 3 0', '0 0', 50, 123],
['test/images/room.png', 5, 1.0, '0 3 10 20 17 10 0', '0 -2 -8 -6 0 2 5 3 0', '0 -0.02 -0.09 -0.16 -0.09 0', 50, 123],
['test/images/castle.png', 0, 1.0, '0 30', '0 -1 -5 -4 0 1 5 4 0', '0 -0.2', 50, 123],
]
max_seed = 2 ** 31
def download_model():
REPO_ID = 'Drexubery/ViewCrafter_25'
filename_list = ['model.ckpt']
for filename in filename_list:
local_file = os.path.join('./checkpoints/', filename)
if not os.path.exists(local_file):
hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/', force_download=True)
download_model()
parser = get_parser() # infer_config.py
opts = parser.parse_args() # default device: 'cuda:0'
opts.save_dir = './'
os.makedirs(opts.save_dir,exist_ok=True)
test_tensor = torch.Tensor([0]).cuda()
opts.device = str(test_tensor.device)
# install pytorch3d
pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
version_str="".join([
f"py3{sys.version_info.minor}_cu",
torch.version.cuda.replace(".",""),
f"_pyt{pyt_version_str}"
])
print(version_str)
os.system(f"{sys.executable} -m pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html")
os.system("mkdir -p checkpoints/ && wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth -P checkpoints/")
print(f'>>> System info: {version_str}')
from viewcrafter import ViewCrafter
def viewcrafter_demo(opts):
css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height:576px} #random_button {max-width: 100px !important}"""
image2video = ViewCrafter(opts, gradio = True)
image2video.run_gradio = spaces.GPU(image2video.run_gradio, duration=300)
with gr.Blocks(analytics_enabled=False, css=css) as viewcrafter_iface:
gr.Markdown("<div align='center'> <h1> ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis </span> </h1> \
<h2 style='font-weight: 450; font-size: 1rem; margin: 0rem'>\
<a href='https://scholar.google.com/citations?user=UOE8-qsAAAAJ&hl=zh-CN'>Wangbo Yu</a>, \
<a href='https://doubiiu.github.io/'>Jinbo Xing</a>, <a href=''>Li Yuan</a>, \
<a href='https://wbhu.github.io/'>Wenbo Hu</a>, <a href='https://xiaoyu258.github.io/'>Xiaoyu Li</a>,\
<a href=''>Zhipeng Huang</a>, <a href='https://scholar.google.com/citations?user=qgdesEcAAAAJ&hl=en/'>Xiangjun Gao</a>,\
<a href='https://www.cse.cuhk.edu.hk/~ttwong/myself.html/'>Tien-Tsin Wong</a>,\
<a href='https://scholar.google.com/citations?hl=en&user=4oXBp9UAAAAJ&view_op=list_works&sortby=pubdate/'>Ying Shan</a>\
<a href=''>Yonghong Tian</a>\
</h2> \
<a style='font-size:18px;color: #FF5DB0' href='https://github.com/Drexubery/ViewCrafter/blob/main/docs/render_help.md'> [Guideline] </a>\
<a style='font-size:18px;color: #000000' href=''> [ArXiv] </a>\
<a style='font-size:18px;color: #000000' href='https://drexubery.github.io/ViewCrafter/'> [Project Page] </a>\
<a style='font-size:18px;color: #000000' href='https://github.com/Drexubery/ViewCrafter'> [Github] </a> </div>")
#######image2video######
with gr.Tab(label="ViewCrafter_25, 'single_view_txt' mode"):
with gr.Column():
with gr.Row():
with gr.Column():
with gr.Row():
i2v_input_image = gr.Image(label="Input Image",elem_id="input_img")
with gr.Row():
i2v_elevation = gr.Slider(minimum=-45, maximum=45, step=1, elem_id="elevation", label="elevation", value=5)
with gr.Row():
i2v_center_scale = gr.Slider(minimum=0.1, maximum=2, step=0.1, elem_id="i2v_center_scale", label="center_scale", value=1)
with gr.Row():
i2v_d_phi = gr.Text(label='d_phi sequence, should start with 0')
with gr.Row():
i2v_d_theta = gr.Text(label='d_theta sequence, should start with 0')
with gr.Row():
i2v_d_r = gr.Text(label='d_r sequence, should start with 0')
with gr.Row():
i2v_steps = gr.Slider(minimum=1, maximum=50, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
with gr.Row():
i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=max_seed, step=1, value=123)
i2v_end_btn = gr.Button("Generate")
# with gr.Tab(label='Result'):
with gr.Column():
with gr.Row():
i2v_traj_video = gr.Video(label="Camera Trajectory",elem_id="traj_vid",autoplay=True,show_share_button=True)
with gr.Row():
i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True)
gr.Examples(examples=i2v_examples,
inputs=[i2v_input_image, i2v_elevation, i2v_center_scale, i2v_d_phi, i2v_d_theta, i2v_d_r, i2v_steps, i2v_seed],
outputs=[i2v_traj_video,i2v_output_video],
fn = image2video.run_gradio,
cache_examples='lazy',
)
# image2video.run_gradio(i2v_input_image='test/images/boy.png', i2v_elevation='10', i2v_d_phi='0 40', i2v_d_theta='0 0', i2v_d_r='0 0', i2v_center_scale=1, i2v_steps=50, i2v_seed=123)
i2v_end_btn.click(inputs=[i2v_input_image, i2v_elevation, i2v_center_scale, i2v_d_phi, i2v_d_theta, i2v_d_r, i2v_steps, i2v_seed],
outputs=[i2v_traj_video,i2v_output_video],
fn = image2video.run_gradio
)
return viewcrafter_iface
viewcrafter_iface = viewcrafter_demo(opts)
viewcrafter_iface.queue(max_size=10)
viewcrafter_iface.launch()
# viewcrafter_iface.launch(server_name='127.0.0.1', server_port=80, max_threads=1,debug=False)
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